Determine a manufacturing batch

ABSTRACT

Examples disclosed herein relate to determining a manufacturing batch. In one implementation, the manufacturing batch relates to 3D printing. A processor may determine component parts of a product that. In one implementation, a processor determines a batch of the component parts related to different products based on a comparison to other potential batches.

BACKGROUND

Manufacturing may involve making batches of products together, such asbatches of products that may be made with the same equipment. 3Dprinting is a type of manufacturing technology performed by layeringmaterial using additive manufacturing technology, such as printing orselected laser sintering. In some cases, 3D printing objects of the samematerial may be created together in a batch where the same type ofmaterial is layered to create objects associated with differentproducts.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings describe example embodiments. The following detaileddescription references the drawings, wherein:

FIG. 1 is a block diagram illustrating one example of a computing systemto determine manufacturing batches for 3D printing manufacturing basedon the amount of support material used.

FIG. 2 is a block diagram illustrating one example of a method todetermine manufacturing batches for 3D printing manufacturing based onthe amount of support material used.

FIG. 3 is a block diagram illustrating one example of a computing systemto determine a batch of parts to manufacture using 3D printertechnology.

FIG. 4 is flow chart illustrating one example of a method to determine abatch of parts to manufacture using 3D printer technology.

FIG. 5 is a block diagram illustrating one example of a computing systemto determine a manufacturing batch based on component parts of aproduct.

FIG. 6 is a flow chart illustrating one example of a method to determinea manufacturing batch based on component parts of a product.

DETAILED DESCRIPTION

A manufacturing entity may group parts from different products intobatches for manufacturing, such as where parts of the same material aremanufactured together. For example, a 3D printing batch may includeparts of different products manufactured together such that the partsmay be disassembled from one another and may be reassembled in theirassociated products at the completion of the process. In oneimplementation, batches of parts manufactured by 3D printing technologymay be automatically recommended, such as based on a weighted scoretaking into account multiple factors. For example, the batches may beselected in a manner that minimizes the amount of support material useddue to the particular parts in the batch and the placement in the batchcausing the previously manufactured parts to provide support for latermanufactured parts. Other selection criteria may include, for example,selecting a batch such that includes the greatest number of partscompared to other potential batches and/or selecting a batch thatincludes parts related to higher priority orders.

In one implementation, a machine learning method is applied to determinecomponent parts of a product of the same material that may bemanufactured together. In one implementation, parts of the same materialmay be divided into subparts to be manufactured separately andrecombined based on the component parts of similar previouslymanufactured products. For example, there may multiple ways to divide apart of the same material into subparts, and the set of subpartsselected for manufacturing may be determined when selecting a potentialbatch. In some cases, the component parts and/or component subparts ofthe different products may then be automatically grouped into batchesfor manufacturing, for example, such that each batch includes parts ofdifferent products created from the same material.

FIG. 1 is a block diagram illustrating one example of a computing system100 to determine manufacturing batches for 3D printing manufacturingbased on the amount of support material used. For example, 3D printingmay involve adding layers of material to one another using printingtechnology. Parts of the same material may be manufactured together.Supporting material may be used to support parts that do not attach tothe base of a product and may be used to hold the different parts inplace to prevent gravity from causing problems with the product duringmanufacturing. In a batch of parts of different products that are notconnected to one another in a single product, support material may beused to support the different parts in the batch. The support materialmay then be dissolved such that the parts may then be disassembled fromthe supporting material and reassembled into their respective products.Such a system may be useful, for example, in made-to-order commercialfulfillment setting where multiple orders are received for manufacturingat the same facility with different service level agreements.

The computing system 100 may include a processor 101 and amachine-readable storage medium 102. The computing system 100 mayrecommend parts from the different products to be manufactured in thesame batch based on a comparison of the amount of support material andassembly effort for different batches of parts.

The processor 101 may be a central processing unit (CPU), asemiconductor-based microprocessor, or any other device suitable forretrieval and execution of instructions. As an alternative or inaddition to fetching, decoding, and executing instructions, theprocessor 101 may include one or more integrated circuits (ICs) or otherelectronic circuits that comprise a plurality of electronic componentsfor performing the functionality described below. The functionalitydescribed below may be performed by multiple processors.

The processor 101 may communicate with the machine-readable storagemedium 102. The machine-readable storage medium 102 may be any suitablemachine readable medium, such as an electronic, magnetic, optical, orother physical storage device that stores executable instructions orother data (e.g., a hard disk drive, random access memory, flash memory,etc.). The machine-readable storage medium 102 may be, for example, acomputer readable non-transitory medium. The machine-readable storagemedium 102 may include support material determination instructions 103,assembly information determination instructions 104, and batchdetermination instructions 105.

The support material determination instructions 103 may includeinstructions used to determine support material used for manufacturing aset of parts in a particular manner with 3D printing technology. Forexample, where multiple parts of the same material are manufacturedtogether, support material may be used to provide padding until anintermediate part of a different material is manufactured and assembledinto the product. In some cases, support material may be used to providesupport between parts such that the parts from the different products donot intersect during the production process. In one implementation, theprocessor 101 determines a potential batch of parts to be manufacturedtogether and determines an arrangement for manufacturing the parts inthe batch, such as determining relative part placement and orientationwith respect to the build direction.

The part placement and/or part orientation may be optimized, forexample, based on providing more parts in a batch and/or based on themechanical properties of the batch. A division of parts into subpartsmay be determined, for example, based on the method described in FIG. 6.The part placement and division of parts into subparts may affect howmuch support material is used. In some cases a previously manufacturedpart may provide support for a later manufactured part such that lesssupport material is used. The processor may determine the amount ofsupport material used to adequately support the parts in the determinedarrangement in a manner that the parts have sufficient space betweenthem and do not overlap. For example, less support material may bedesirable, but the manner in which support material is used may resultin more assembly effort to reconstruct the products from the batches ofparts. In addition, breaking parts into smaller components such thatpart placement may be done in a manner using less support material mayresult in greater assembly effort to re-assemble the component partsinto the desired products.

The assembly information determination instructions 104 may includeinstructions to determine assembly information, such as assembly time,number of people, or other assembly effort metrics, associated with apotential batch. The processor may determine an amount of assemblyeffort for reassembling the parts from the batch into the products, suchas where greater assembly time is used when the parts are placed indifferent batches or positions. In some cases, the assembly effort maybe based on the number of components that a part is broken into, such aswhere a part is manufactured in two pieces in order to provide supportfor other parts and lessen the amount of support material. For example,a part of a single material may be manufactured as a single part orsplit into subparts. Manufacturing with more subparts may allow for morepotential configurations and result in less supporting material used andmore assembly effort. Additional factors may also be taken into accountin determining a manufacturing batch, such as maximizing the number ofparts in a batch, maximizing number of parts of a certain type in abatch, and/or maximizing the number of parts associated with aparticular type of service level agreement.

The batch determination instructions 105 may include instructions todetermine how to batch parts from multiple products based on acomparison of the amount of support material used and assemblyinformation for a potential batch. For example, the processor maydetermine multiple potential ways to batch a set of parts, and thegroups of batches for manufacturing the parts for the set of productsmay be compared based on the amount of support material associated withthe particular group of batches and the assembly effort used. Multiplepotential batch combinations may be determined where the batches includedifferent parts and/or part placements, and a batch combination may beselected based on a comparison of the support material associated witheach batch combination and the assembly effort to assemble the finalproduct from the parts of the different batches.

In one implementation, the machine-readable storage medium 102 furtherincludes assembly instructions related to how to assemble the parts fromthe different batches into the completed products. The machine-readablestorage medium 102 may include post assembly instructions related toadditional processing for a product after the component parts have beencombined. For example, a surface finishing may be applied to the surfaceof the product.

FIG. 2 is a flow chart illustrating one example of a method to determinemanufacturing batches for 3D printing manufacturing based on the amountof support material used. For example, product A may include parts 1, 2,and 3, product B may include parts 4, 5, and 6, and product C mayinclude parts 7 and 8. Some of the different parts may be made from thesame material, such as where parts 1, 3, 6, and 8 are made from the samematerial. A processor may determine which parts to include in a 3Dmanufacturing batch and how to place them within a batch. For example,parts 1, 3, 6, and 8 may be manufactured together or parts 1 and 3 maybe manufactured together and parts 6 and 8 may be manufactured together.The processor may determine a placement of the different parts in abatch and the amount of supporting material used for the particularplacement. The method may be implemented, for example, by the processor101 from FIG. 1.

Beginning at 200, a processor determines the amount of support materialused to create different sets of 3D printing batches of the same set ofparts. For example, the processor may receive a list of component partsassociated with a set of products and a list of potential subparts thatthe components parts may be broken into. The processor may receivematerial information such that parts of the same material may be groupedtogether. In some cases, different products may include some parts thatare the same that are created from the same material. The processor mayform sets of manufacturing batches from the parts such that parts of thesame material are batched together. The processor may further determinepart placement within the batches for 3D printing.

The parts may be placed and oriented such that some parts providesupport for other parts, and extra supporting material may be used toprevent parts from intersecting. The amount of support material may bedetermined, for example, by creating a 3D print STL file associated withthe components and placement of parts in a batch and determining theamount of support material based on surface triangles indicated in theSTL file.

Continuing to 201, the processor determines assembly information for thedifferent sets of batches. For example, the assembly effort may begreater where the component parts are divided into subparts, such as forthe purpose of orienting the parts to support one another to result inless extraneous supporting material. The assembly information may bedetermined, for example, based on stored assembly information related tosimilar products and component divisions. In one implementation, theassembly information is determined based on the number of componentparts. In some cases, the assembly time is determined based on the setof batches instead of or in addition to the assembly time based on asingle batch. For example, dividing the manufacturing into more batchesmay result in greater assembly time to assemble the product from morebatches. The time to assemble may be increased based on waiting time foradditional batches.

Continuing to 202, the processor selects one of the sets of batches foroutput based on a comparison of the amount of supporting material andassembly information associated with the sets of batches. For example, ascore may be associated with each of the sets of batches where the scorerepresents a comparison of the supporting material and assemblyinformation, such as where more supporting material and greater assemblysubtracts from the score. The potential batches and scores may beupdated as more orders are received. For example, batch information maybe output for existing orders and may be updated when additional ordersare received.

In one implementation, additional factors are considered in selecting apotential batch from the sets of batches. For example, the number ofparts in the batch, the parts of a certain type in a batch, or theservice level agreement associated with products with the parts in thebatch may be considered. The factors may be weighted according to adefault weighting scheme or based on user input providing informationabout the relative importance of the factors.

Information related to the selected batch may be output formanufacturing. STL files may be created for the batches within theselected set of batches. The STL files may be sent to 3D printers formanufacturing the parts associated with the desired products.

FIG. 3 is a block diagram illustrating one example of a computing system300 to determine a batch of parts to manufacture using 3D printertechnology. The batch may include parts from different products that aremade from the same material such that the parts may be manufacturedtogether. The part placement and orientation may also be determined. Abatch may be selected from a set of potential batches based on a scoreassociated with the batch indicating the desirability of the particularbatch. For example, a potential batch may be determined and it may beselected for manufacturing where a score associated with the batch isgreater than a score associated with another batch. Factors such as theamount of supporting material, the number of parts in a batch, and otherfactors may be taken into account. The computing system 300 includes aprocessor 301 and a machine-readable storage medium 302.

The processor 301 may be a central processing unit (CPU), asemiconductor-based microprocessor, or any other device suitable forretrieval and execution of instructions. As an alternative or inaddition to fetching, decoding, and executing instructions, theprocessor 301 may include one or more integrated circuits (ICs) or otherelectronic circuits that comprise a plurality of electronic componentsfor performing the functionality described below. The functionalitydescribed below may be performed by multiple processors.

The processor 301 may communicate with the machine-readable storagemedium 302. The machine-readable storage medium 302 may be any suitablemachine readable medium, such as an electronic, magnetic, optical, orother physical storage device that stores executable instructions orother data (e.g., a hard disk drive, random access memory, flash memory,etc.). The machine-readable storage medium 302 may be, for example, acomputer readable non-transitory medium. The machine-readable storagemedium 302 may include potential batch determination instructions 303,part placement determination instructions 304, batch validationinstructions 305, score determination instructions 306, score comparisoninstructions 307, and batch output instructions 308.

The potential batch determination instructions 303 may includeinstructions for determining a potential batch of components tomanufacture together. Multiple potential batches may be determined andcompared, and batches may be selected for manufacturing from the list ofpotential batches. For example, multiple different sets of parts may bedetermined. The part placement determination instructions 304 includesinstructions to determine part placement and orientation for partsmanufactured together in the same batch, such as where the parts may insome cases support one another in different manners. The batchvalidation instructions 305 include instructions to verify the potentialbatch, such as to confirm that support material is used in a manner thatprevents parts from overlapping. The score determination instructions306 include instructions to associate a score with the potential batchindicating the desirability of the particular batch based on a set offactors. The score comparison instructions 307 include instructions tocompare scores of different batch combinations to select a batch or setof batches to be manufactured, The batch output instructions 308includes instructions to output information about a selectedmanufacturing batch.

The machine-readable storage medium 102 may include assemblyinstructions related to how to assemble the parts from the differentbatches into the completed products. For example, the parts from thedifferent batches may be automatically or manually combined into thedesired products based on assembly specifications. The machine-readablestorage medium 102 may include post assembly instructions related toadditional processing for a product after the component parts have beencombined. For example, a surface finishing may be applied to the surfaceof the product. The post processing may occur automatically or may berelated to instructions output to be manually performed, such as wherethe post processing instructions are displayed by the processor 101.

FIG. 4 is flow chart illustrating one example of a method to determine abatch of parts to manufacture using 3D printer technology. For example,a set of parts may be associated with product orders. A subset of theparts may be made from the same material. The subset of parts of thesame material may be grouped together in different batches formanufacturing. Some batches may be more desirable than others, such asdue to the type of parts, the resulting assembly time aftermanufacturing, or an increase cost associated with the particular batchcombination. A batch may be automatically recommended by a processorthat creates and compares potential part combinations forming differentmanufacturing batches. The method may be implemented, for example, bythe processor 301 of FIG. 3.

Beginning at 400, a processor determines a first potential batch ofparts to be manufactured by 3D printing technology. The manufacturingtechnology may include, for example, pure 3D printing technology orhybrid 3D printing technology. The first potential batch may be selectedin any suitable manner, such as using a genetic method. The geneticmethod may include creating multiple gene representations of differentbatches of parts combined into a chromosome representation of the set ofbatches used to create the parts associated with a group of products.

Continuing to 401, the processor determines part placement of the partsin the first potential batch. The part placement may include informationabout the relative position of parts in a product and the relativeorientation of parts of the product. For example, with 3D printingtechnology, the manufactured parts may use support material to keepparts from intersecting with one another. In some cases, the parts maybe placed and oriented in a manner such that the a first part providessupport for a second part manufactured at a later time, and less or nosupport material is used between the two parts.

In one implementation a set of potential parts and subparts may bereceived, such as from a storage. In some cases, the parts and subpartsmay be determined based on, for example, the method described in FIG. 6below. The processor may determine how to batch the parts of the samematerial and determine whether to divide some or all of the parts intosubparts. In some cases, there may be multiple subpart options for thesame part, and the processor determines which subpart division resultsin a better batch combination, such as due to less supporting materialbeing used. In some cases, the processor takes into account both theamount of supporting material and the assembly time associated with thebatch.

In one implementation, a genetic method is used where a potential batchis selected, and batching, placement, and orientation decisions are thenmade by the processor for the parts selected for the potential batch.The processor may determine the amount and placement of support materialand simulate the parts in the batch being manufactured together.

Continuing to 402, the processor validates the first potential batch.Validating the batch may include for example, determining that thepotential part placement meets certain criteria to ensure a plausiblebatch, such as the parts being a safe distance apart withoutintersection between the parts. For example, if a potential batch ofparts cannot be placed in a manner that may be validated, the methodgoes no further with the particular batch, and the processor determinesanother potential batch. For example, the batch may include a placementsuch that enough room is not left for an adequate amount of supportingmaterial. The validation may include other factors, such as thelikelihood of the resulting parts' mechanical properties meeting minimumrequirements of service contracts related to the products with parts inthe batch.

Continuing to 403, the processor determines a score associated with thefirst potential batch if the potential batch is validated. The score mayindicate a desirability level of the first potential batch and placementbased on a set of factors. The factors may be supplied or weighted basedon user input. In some cases, the factors are automatically adjusted,such as based on a season, service level agreement, or information aboutthe condition of equipment. The factors may include, for example, thenumber of parts in the batch, the parts of a certain type in the batch,and the parts associated with a particular service level agreement ofproducts associated with parts in the batch. The score may include afactor based on the amount of support material used. For example, inbatch placement where previously manufactured parts provide less supportto later manufactured parts, more support material may be used toprovide the proper spacing between the components. The amount of supportmaterial may be determined by creating a 3D print STL file associatedwith the components and placement of a batch and determining the amountof support material based on surface triangles indicated in the STLfile. The score may include a factor associated with the assembly timeor effort. For example, a batch that breaks a part into more componentsmay involve greater assembly effort. The score may weigh the lessersupport material against the possibility of greater assembly cost. Therelative importance of the different factors may be adjusted based on aweighting scheme. In some cases, user input may be provided to selectthe factors and/or weight them relative to one another.

Continuing to 404, the processor compares the score of the firstpotential batch to scores associated with other potential batches. Forexample, the batch with the greatest or least score may be selected. Insome cases, a genetic method is applied that continually creates newbatches with different parts and/or different part placement. Thegenetic method may stop optimizing the batches when a signal is receivedthat indicates that output is desired, such as when the output STL filefor the 3D printing is requested. The parts included in the batch may bedeleted from the set of parts to be manufactured, and a genetic methodmay be applied to determine the next batch to be manufactured.

Continuing to 405, the processor selects the first batch for output to a3D printer based on the score comparison. For example, the batch withthe least or greatest score may be selected. Additional processing maybe applied to the batch before output. For example, the processor maygenerate slicing planes and support layers for the batch. Outputtinginformation about the batch may include storing, transmitting, ordisplaying information about the parts in the selected batch. Theprocessor may create or receive and STL file related to the selectedbatch. Multiple batches including the different products may be outputsuch that between the different batches the parts to compose therequested products are included. In some cases, the STL file mayautomatically be routed to the proper equipment.

FIG. 5 is a block diagram illustrating one example of a computing system500 to determine a manufacturing batch based on component parts of aproduct. For example, a manufacturing order may be divided intocomponent parts such that parts of the same material may be manufacturedtogether. The component parts may be combined from the different batchesafter manufacturing to create the desired product. In some cases,multiple product orders may be combined such that component pieces ofthe same material are manufactured in the same batch, and themanufactured pieces may then be added to different products.

The computing system 500 includes a processor 501, a storage 507, and amachine-readable storage medium 502. The storage 507 may include, forexample, past manufacturing practice information 508. The pastmanufacturing practice information 508 may include any suitableinformation about how past manufacturing orders were fulfilled. Forexample, the past manufacturing practice information 508 may includeinformation about how orders were combined, which machines manufacturedwhich pieces of the products, or how parts of a single material weredivided into subparts.

The storage 507 may communicate directly with the processor 501, such aswhere the storage 507 and the processor 501 are in a single electronicdevice, or the storage 507 and the processor 501 may communicate via anetwork, such as in a cloud based system.

The processor 501 may be a central processing unit (CPU), asemiconductor-based microprocessor, or any other device suitable forretrieval and execution of instructions. As an alternative or inaddition to fetching, decoding, and executing instructions, theprocessor 501 may include one or more integrated circuits (ICs) or otherelectronic circuits that comprise a plurality of electronic componentsfor performing the functionality described below. The functionalitydescribed below may be performed by multiple processors.

The processor 501 may communicate with the machine-readable storagemedium 502. The machine-readable storage medium 502 may be any suitablemachine readable medium, such as an electronic, magnetic, optical, orother physical storage device that stores executable instructions orother data (e.g., a hard disk drive, random access memory, flash memory,etc.). The machine-readable storage medium 502 may be, for example, acomputer readable non-transitory medium. The machine-readable storagemedium 502 may include pattern determining instructions 503, productcomponent part determining instructions 504, and part batch determininginstructions 505.

The pattern determining instructions 503 may include instructions aboutdetermining manufacturing patterns based on the past manufacturinginformation 108. For example, a particular manufacturing environment maymanufacture items different according to the type of equipment, thecapacity of the equipment, or the state of the equipment. Themanufacturing environment may have policies related to the particularenvironment, such as a prioritization of particular types of products.The determined patterns may be any suitable pattern information relatedto patterns as to how pieces of manufacturing orders are batchedtogether. The patterns may be determined using a machine learningmethod. In one implementation, the manufacturing patterns also relate tobreaking an order into a list of products. For example, a machinelearning method may be used to determine how orders are broken intoproducts in the particular manufacturing environment, such as related toa format of order documents that is typically received by the particularmanufacturer.

The product component part determining instructions 504 may includeinstructions to determine individual components of products of the samematerial that may be manufactured in the same batch. For example, thedetermined patterns may be used to determine how a product may bedivided into different components. A specification of a product mayindicate different portions of a different product, and the determinedpatterns may be used to determine how portions of the same material maybe broken in individual components and sub-components. For example, apart of a product of the same material may be created as a single partor broken into subparts that can later be combined into the single part.In some cases, there are multiple ways to break the part into subparts,and different options may be determined by the processor. The differentsubpart combinations may be taken into account, for example, in themethod of FIG. 4. The product component part determining instructions504 may include instructions related to dividing parts of a product ofthe same material into subparts based on the determined patterns. Forexample, the parts of the same material may be manufactured separatelyand then recombined when the product is assembled. The part may bedivided into subparts, for example, in order to manufacture the partsmore quickly among multiple machines.

The part batch determining instructions 505 may include instructions todetermine component parts of the same material from different productsand/or orders for manufacturing components of the same materialtogether. For example, the determined component parts of the samematerial may be manufactured together. The component parts of the samematerial may be grouped into batches for manufacturing. In someimplementations, the grouping into batches is done based on determinedprevious grouping patterns.

FIG. 6 is a flow chart illustrating one example of a method to determinemanufacturing batches based on component parts of a product. The methodmay be applied to a cloud system for receiving multiple manufacturingorders, and the method may be used to manufacture parts from thedifferent orders together. As an example, a made-to-order commercialfulfillment setting may receive multiple for manufacturing products atthe same facility where the different orders have different servicelevel agreements. A manner of dividing sections of a product of the samematerial into components may be based on the manner in which apreviously manufactured similar product was divided into components ofthe same material. The method may be implemented, for example, by theprocessor 501 of FIG. 5.

Beginning at 600, a processor determines patterns of component partsassociated with different products based on information related tomanufacturing practices of past products. For example, a particularmanufacturing entity may have practices related to how individualcomponents of a product are manufactured and then assembled together,such as with glue, fire, or bounding material. An analysis of productspecification and fabrication capability may be conducted by analyzingprevious orders to the manufacturing entity. For example, a product maybe divided into a particular number or shape of components based on theequipment capabilities. Additional factors may be considered, such aswhere the same product is divided into components differently accordingto the desired production speed evidenced by a service level agreementdeadline.

Continuing to 601, the processor determines individual components of acurrent product for manufacturing based on the determined patterns. Inone implementation, component parts of products are determined based onthe similarity to previously manufactured products. For example, aproduct and/or order is compared to stored information related to themanufacturing of products. Information related to a similar past orderand/or a set of similar past orders may be used to determine a likelydivision of component parts for manufacturing. The similarity may bebased on factors related to the product to be manufactured, the type oforder or agreement, or the type of equipment to be used to produce theorder. A similarity score may be determined, and past orders with thehighest similarity or a similarity to the current order above athreshold may be considered. In some cases, the order is compared tosummary information related to classes of past orders instead of or inaddition to comparing the current order to individual previous orders.

A current order may be broken into components in the same or similarmanner as the most similar past order or a past order with a similarityabove a threshold. In some cases, the processor first divides an orderinto products, such as based on a line analysis of the purchaseagreement or invoice, and then determines component parts associatedwith each of the products.

In one implementation, the processor divides a product into a list ofparts and where each part is made from a single material. The processormay further divide the parts made from the single material into amultiple parts of the material that may be assembled aftermanufacturing. The processor may determine multiple manners of dividingthe part of the same material into subparts, and the processor mayselect one of the subpart combinations or select to manufacture the partas a single part

In some cases, the manufacturing practice may involve 3D printing, andthe processor may determine partition planes related to the componentparts. The recommended partition planes may be based on partition planesused in previous similar orders/products. The processor may furtherdetermine assembly information based on previous similarorders/products. For example, particular features may be added to makeassembly simpler when a particular set of partition planes are used.

Moving to 602, the processor determines a batch of the determinedcomponents to be manufactured together. For example, component parts ofthe same material from different products and/or orders may bemanufactured in the same batch. In some cases, where the parts aremanufactured such that parts are divided into subparts of the samematerial, subparts may be dovetailed to aid in assembly of the subpartsinto the part of the same material. In the case of 3D printing, thebatch may be determined according to the method of FIGS. 2 and/or 4.

1. A method, comprising: determining a first potential batch of parts to be manufactured by 3D printing technology, wherein the parts are associated with multiple products; determining part placement of the parts in the first potential batch; validating the first potential batch, wherein the validation comprises determining the distance between parts in the part placement; determining a score associated with the first potential batch; comparing the score of the first potential batch to scores associated with other potential batches; and selecting the first batch for output to a 3D printer based on the score comparison.
 2. The method of claim 1, further comprising dividing a product into parts to be placed in potential batches based on the parts of a past product order with a similarity to the product above a threshold.
 3. The method of claim 1 wherein the score of a batch is based on at least one of: the number of parts in the batch, the parts of a certain type in the batch, the parts associated with a particular service level agreement of products associated with parts in the batch; and the amount of support material in the batch.
 4. The method of claim 1, further comprising: creating an STL file associated with the first potential batch; determining the amount of support material based on surface triangles indicated in the STL file; and determining the core based on the amount of support material
 5. The method of claim 1, further comprising determining the first potential batch based on a genetic method to create a potential batch from a list of parts.
 6. The method of claim 5, wherein the genetic method comprises creating multiple gene representations of different batches of parts combined into a chromosome representation of the batches used to create the parts associated with a group of products.
 7. A machine-readable non-transitory storage medium comprising instructions executable by a processor to: determine the amount of support material used to create different sets of D printing batches of the same set of parts; determine assembly information for the different sets of batches; and select one of the sets of batches for output based on a comparison of the amount of support material and assembly information associated with the sets of batches.
 8. The machine-readable non-transitory storage medium of claim 7, further comprising instructions to determine the component parts of a product in a received order based on the similarity of the product to a product in a past order.
 9. The machine-readable non-transitory storage medium of claim 7, further comprising instructions to determine 3D print partition planes associated with each of the sets of batches.
 10. The machine-readable non-transitory storage medium of claim 7, wherein instruction to select one of the batches further comprises instructions to select one of the batches based on weighted factors including at least one of: the number of parts in the batch, the parts of certain type in the batch, and the parts associated with a particular service level agreement of products associated with parts in the batch.
 11. A computing system, comprising: a storage to store information related to manufacturing practices of past products; a processor to: determine patterns of component parts associated with different products based on the stored information: determine individual components of a current product for manufacturing based on the determined patterns; and determine a batch of the determined components to be manufactured together.
 12. The computing system of claim 11, wherein determining individual components comprises determining individual components based on a pattern associated with a past product with a similarity level above a threshold with the current product.
 13. The computing system of claim 11, wherein the processor is further to determine 3D print partition planes to be associated with the determined set of components based on partition planes associated with the past products.
 14. The computing system of claim 11, wherein the processor is further to determine a manner of dovetailing the components to be reassembled to create a product.
 15. The computing system of claim 11, wherein determining a batch comprises: determining the amount of support material used to create different sets of D printing batches of the same set of parts; determining assembly information for the different sets of batches; and selecting one of the sets of batches for output based on a comparison of the amount of support material and assembly information associated with the sets of batches 